Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations74
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory59.8 KiB
Average record size in memory827.8 B

Variable types

Numeric9
Text5
Categorical8

Alerts

balls_left is highly overall correlated with first_ings_score and 2 other fieldsHigh correlation
first_ings_score is highly overall correlated with balls_left and 3 other fieldsHigh correlation
first_ings_wkts is highly overall correlated with first_ings_score and 1 other fieldsHigh correlation
highscore is highly overall correlated with first_ings_score and 1 other fieldsHigh correlation
match_result is highly overall correlated with balls_left and 2 other fieldsHigh correlation
second_ings_score is highly overall correlated with first_ings_score and 2 other fieldsHigh correlation
second_ings_wkts is highly overall correlated with balls_leftHigh correlation
team1 is highly overall correlated with venueHigh correlation
team2 is highly overall correlated with toss_winnerHigh correlation
toss_winner is highly overall correlated with team2High correlation
venue is highly overall correlated with team1High correlation
stage is highly imbalanced (78.1%) Imbalance
match_result is highly imbalanced (75.5%) Imbalance
match_id is uniformly distributed Uniform
team1 is uniformly distributed Uniform
team2 is uniformly distributed Uniform
match_id has unique values Unique
second_ings_wkts has 2 (2.7%) zeros Zeros
balls_left has 26 (35.1%) zeros Zeros

Reproduction

Analysis started2025-06-13 15:34:16.317984
Analysis finished2025-06-13 15:34:36.652464
Duration20.33 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

match_id
Real number (ℝ)

Uniform  Unique 

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.5
Minimum1
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-06-13T21:04:36.841783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.65
Q119.25
median37.5
Q355.75
95-th percentile70.35
Maximum74
Range73
Interquartile range (IQR)36.5

Descriptive statistics

Standard deviation21.505813
Coefficient of variation (CV)0.57348835
Kurtosis-1.2
Mean37.5
Median Absolute Deviation (MAD)18.5
Skewness0
Sum2775
Variance462.5
MonotonicityStrictly increasing
2025-06-13T21:04:37.169915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.4%
56 1
 
1.4%
54 1
 
1.4%
53 1
 
1.4%
52 1
 
1.4%
51 1
 
1.4%
50 1
 
1.4%
49 1
 
1.4%
48 1
 
1.4%
47 1
 
1.4%
Other values (64) 64
86.5%
ValueCountFrequency (%)
1 1
1.4%
2 1
1.4%
3 1
1.4%
4 1
1.4%
5 1
1.4%
6 1
1.4%
7 1
1.4%
8 1
1.4%
9 1
1.4%
10 1
1.4%
ValueCountFrequency (%)
74 1
1.4%
73 1
1.4%
72 1
1.4%
71 1
1.4%
70 1
1.4%
69 1
1.4%
68 1
1.4%
67 1
1.4%
66 1
1.4%
65 1
1.4%

date
Text

Distinct62
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2025-06-13T21:04:37.714660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.216216
Min length10

Characters and Unicode

Total characters904
Distinct characters26
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)67.6%

Sample

1st rowMarch 22,2025
2nd rowMarch 23,2025
3rd rowMarch 23,2025
4th rowMarch 24,2025
5th rowMarch 25,2025
ValueCountFrequency (%)
april 37
25.0%
may 23
15.5%
march 12
 
8.1%
23,2025 4
 
2.7%
25,2025 4
 
2.7%
30,2025 4
 
2.7%
27,2025 4
 
2.7%
18,2025 3
 
2.0%
24,2025 3
 
2.0%
22,2025 3
 
2.0%
Other values (32) 51
34.5%
2025-06-13T21:04:38.527124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 186
20.6%
0 93
10.3%
5 82
9.1%
74
 
8.2%
, 74
 
8.2%
r 49
 
5.4%
A 37
 
4.1%
i 37
 
4.1%
l 37
 
4.1%
p 37
 
4.1%
Other values (16) 198
21.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 434
48.0%
Lowercase Letter 248
27.4%
Space Separator 74
 
8.2%
Other Punctuation 74
 
8.2%
Uppercase Letter 74
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 49
19.8%
i 37
14.9%
l 37
14.9%
p 37
14.9%
a 35
14.1%
y 23
9.3%
c 12
 
4.8%
h 12
 
4.8%
u 2
 
0.8%
n 2
 
0.8%
Decimal Number
ValueCountFrequency (%)
2 186
42.9%
0 93
21.4%
5 82
18.9%
1 24
 
5.5%
3 14
 
3.2%
7 8
 
1.8%
9 7
 
1.6%
8 7
 
1.6%
4 7
 
1.6%
6 6
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
A 37
50.0%
M 35
47.3%
J 2
 
2.7%
Space Separator
ValueCountFrequency (%)
74
100.0%
Other Punctuation
ValueCountFrequency (%)
, 74
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 582
64.4%
Latin 322
35.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 49
15.2%
A 37
11.5%
i 37
11.5%
l 37
11.5%
p 37
11.5%
M 35
10.9%
a 35
10.9%
y 23
7.1%
c 12
 
3.7%
h 12
 
3.7%
Other values (4) 8
 
2.5%
Common
ValueCountFrequency (%)
2 186
32.0%
0 93
16.0%
5 82
14.1%
74
 
12.7%
, 74
 
12.7%
1 24
 
4.1%
3 14
 
2.4%
7 8
 
1.4%
9 7
 
1.2%
8 7
 
1.2%
Other values (2) 13
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 186
20.6%
0 93
10.3%
5 82
9.1%
74
 
8.2%
, 74
 
8.2%
r 49
 
5.4%
A 37
 
4.1%
i 37
 
4.1%
l 37
 
4.1%
p 37
 
4.1%
Other values (16) 198
21.9%

venue
Categorical

High correlation 

Distinct13
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Narendra Modi Stadium, Ahmedabad
9 
Ekana Cricket Stadium, Lucknow
8 
Eden Gardens, Kolkata
7 
Wankhede Stadium, Mumbai
7 
Sawai Mansingh Stadium, Jaipur
7 
Other values (8)
36 

Length

Max length45
Median length33
Mean length30.527027
Min length21

Characters and Unicode

Total characters2259
Distinct characters44
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st rowEden Gardens, Kolkata
2nd rowRajiv Gandhi International Stadium, Hyderabad
3rd rowMA Chidambaram Stadium, Chennai
4th rowACA-VDCA Cricket Stadium, Vishakhapatnam
5th rowNarendra Modi Stadium, Ahmedabad

Common Values

ValueCountFrequency (%)
Narendra Modi Stadium, Ahmedabad 9
12.2%
Ekana Cricket Stadium, Lucknow 8
10.8%
Eden Gardens, Kolkata 7
9.5%
Wankhede Stadium, Mumbai 7
9.5%
Sawai Mansingh Stadium, Jaipur 7
9.5%
Arun Jaitley Stadium, Delhi 7
9.5%
Rajiv Gandhi International Stadium, Hyderabad 6
8.1%
MA Chidambaram Stadium, Chennai 6
8.1%
M. Chinnaswamy Stadium, Bangalore 6
8.1%
New PCA Cricket Stadium, Mullanpur 6
8.1%
Other values (3) 5
6.8%

Length

2025-06-13T21:04:38.823997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
stadium 67
23.0%
cricket 16
 
5.5%
narendra 9
 
3.1%
ahmedabad 9
 
3.1%
modi 9
 
3.1%
ekana 8
 
2.7%
lucknow 8
 
2.7%
mansingh 7
 
2.4%
delhi 7
 
2.4%
jaitley 7
 
2.4%
Other values (27) 144
49.5%

Most occurring characters

ValueCountFrequency (%)
a 303
 
13.4%
217
 
9.6%
i 174
 
7.7%
d 148
 
6.6%
n 129
 
5.7%
t 113
 
5.0%
u 110
 
4.9%
e 106
 
4.7%
m 104
 
4.6%
r 90
 
4.0%
Other values (34) 765
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1636
72.4%
Uppercase Letter 324
 
14.3%
Space Separator 217
 
9.6%
Other Punctuation 80
 
3.5%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 303
18.5%
i 174
10.6%
d 148
9.0%
n 129
7.9%
t 113
 
6.9%
u 110
 
6.7%
e 106
 
6.5%
m 104
 
6.4%
r 90
 
5.5%
h 62
 
3.8%
Other values (12) 297
18.2%
Uppercase Letter
ValueCountFrequency (%)
S 74
22.8%
C 45
13.9%
M 41
12.7%
A 35
10.8%
G 15
 
4.6%
E 15
 
4.6%
N 15
 
4.6%
J 14
 
4.3%
D 10
 
3.1%
B 8
 
2.5%
Other values (8) 52
16.0%
Other Punctuation
ValueCountFrequency (%)
, 74
92.5%
. 6
 
7.5%
Space Separator
ValueCountFrequency (%)
217
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1960
86.8%
Common 299
 
13.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 303
15.5%
i 174
 
8.9%
d 148
 
7.6%
n 129
 
6.6%
t 113
 
5.8%
u 110
 
5.6%
e 106
 
5.4%
m 104
 
5.3%
r 90
 
4.6%
S 74
 
3.8%
Other values (30) 609
31.1%
Common
ValueCountFrequency (%)
217
72.6%
, 74
 
24.7%
. 6
 
2.0%
- 2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2259
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 303
 
13.4%
217
 
9.6%
i 174
 
7.7%
d 148
 
6.6%
n 129
 
5.7%
t 113
 
5.0%
u 110
 
4.9%
e 106
 
4.7%
m 104
 
4.6%
r 90
 
4.0%
Other values (34) 765
33.9%

team1
Categorical

High correlation  Uniform 

Distinct10
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
PBKS
9 
KKR
8 
GT
8 
RCB
8 
CSK
7 
Other values (5)
34 

Length

Max length4
Median length3
Mean length2.7297297
Min length2

Characters and Unicode

Total characters202
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKKR
2nd rowSRH
3rd rowCSK
4th rowDC
5th rowGT

Common Values

ValueCountFrequency (%)
PBKS 9
12.2%
KKR 8
10.8%
GT 8
10.8%
RCB 8
10.8%
CSK 7
9.5%
DC 7
9.5%
RR 7
9.5%
MI 7
9.5%
LSG 7
9.5%
SRH 6
8.1%

Length

2025-06-13T21:04:39.105242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:04:39.437238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
pbks 9
12.2%
kkr 8
10.8%
gt 8
10.8%
rcb 8
10.8%
csk 7
9.5%
dc 7
9.5%
rr 7
9.5%
mi 7
9.5%
lsg 7
9.5%
srh 6
8.1%

Most occurring characters

ValueCountFrequency (%)
R 36
17.8%
K 32
15.8%
S 29
14.4%
C 22
10.9%
B 17
8.4%
G 15
7.4%
P 9
 
4.5%
T 8
 
4.0%
D 7
 
3.5%
M 7
 
3.5%
Other values (3) 20
9.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 202
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 36
17.8%
K 32
15.8%
S 29
14.4%
C 22
10.9%
B 17
8.4%
G 15
7.4%
P 9
 
4.5%
T 8
 
4.0%
D 7
 
3.5%
M 7
 
3.5%
Other values (3) 20
9.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 202
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 36
17.8%
K 32
15.8%
S 29
14.4%
C 22
10.9%
B 17
8.4%
G 15
7.4%
P 9
 
4.5%
T 8
 
4.0%
D 7
 
3.5%
M 7
 
3.5%
Other values (3) 20
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 36
17.8%
K 32
15.8%
S 29
14.4%
C 22
10.9%
B 17
8.4%
G 15
7.4%
P 9
 
4.5%
T 8
 
4.0%
D 7
 
3.5%
M 7
 
3.5%
Other values (3) 20
9.9%

team2
Categorical

High correlation  Uniform 

Distinct10
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
MI
9 
RCB
8 
PBKS
8 
SRH
8 
RR
7 
Other values (5)
34 

Length

Max length4
Median length3
Mean length2.7027027
Min length2

Characters and Unicode

Total characters200
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRCB
2nd rowRR
3rd rowMI
4th rowLSG
5th rowPBKS

Common Values

ValueCountFrequency (%)
MI 9
12.2%
RCB 8
10.8%
PBKS 8
10.8%
SRH 8
10.8%
RR 7
9.5%
LSG 7
9.5%
CSK 7
9.5%
GT 7
9.5%
DC 7
9.5%
KKR 6
8.1%

Length

2025-06-13T21:04:39.808056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:04:40.131695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
mi 9
12.2%
rcb 8
10.8%
pbks 8
10.8%
srh 8
10.8%
rr 7
9.5%
lsg 7
9.5%
csk 7
9.5%
gt 7
9.5%
dc 7
9.5%
kkr 6
8.1%

Most occurring characters

ValueCountFrequency (%)
R 36
18.0%
S 30
15.0%
K 27
13.5%
C 22
11.0%
B 16
8.0%
G 14
 
7.0%
M 9
 
4.5%
I 9
 
4.5%
P 8
 
4.0%
H 8
 
4.0%
Other values (3) 21
10.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 200
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 36
18.0%
S 30
15.0%
K 27
13.5%
C 22
11.0%
B 16
8.0%
G 14
 
7.0%
M 9
 
4.5%
I 9
 
4.5%
P 8
 
4.0%
H 8
 
4.0%
Other values (3) 21
10.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 200
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 36
18.0%
S 30
15.0%
K 27
13.5%
C 22
11.0%
B 16
8.0%
G 14
 
7.0%
M 9
 
4.5%
I 9
 
4.5%
P 8
 
4.0%
H 8
 
4.0%
Other values (3) 21
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 36
18.0%
S 30
15.0%
K 27
13.5%
C 22
11.0%
B 16
8.0%
G 14
 
7.0%
M 9
 
4.5%
I 9
 
4.5%
P 8
 
4.0%
H 8
 
4.0%
Other values (3) 21
10.5%

stage
Categorical

Imbalance 

Distinct3
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
League
70 
Playoffs
 
3
Final
 
1

Length

Max length8
Median length6
Mean length6.0675676
Min length5

Characters and Unicode

Total characters449
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st rowLeague
2nd rowLeague
3rd rowLeague
4th rowLeague
5th rowLeague

Common Values

ValueCountFrequency (%)
League 70
94.6%
Playoffs 3
 
4.1%
Final 1
 
1.4%

Length

2025-06-13T21:04:40.511014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:04:40.745391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
league 70
94.6%
playoffs 3
 
4.1%
final 1
 
1.4%

Most occurring characters

ValueCountFrequency (%)
e 140
31.2%
a 74
16.5%
L 70
15.6%
g 70
15.6%
u 70
15.6%
f 6
 
1.3%
l 4
 
0.9%
P 3
 
0.7%
y 3
 
0.7%
o 3
 
0.7%
Other values (4) 6
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 375
83.5%
Uppercase Letter 74
 
16.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 140
37.3%
a 74
19.7%
g 70
18.7%
u 70
18.7%
f 6
 
1.6%
l 4
 
1.1%
y 3
 
0.8%
o 3
 
0.8%
s 3
 
0.8%
i 1
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
L 70
94.6%
P 3
 
4.1%
F 1
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 449
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 140
31.2%
a 74
16.5%
L 70
15.6%
g 70
15.6%
u 70
15.6%
f 6
 
1.3%
l 4
 
0.9%
P 3
 
0.7%
y 3
 
0.7%
o 3
 
0.7%
Other values (4) 6
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 449
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 140
31.2%
a 74
16.5%
L 70
15.6%
g 70
15.6%
u 70
15.6%
f 6
 
1.3%
l 4
 
0.9%
P 3
 
0.7%
y 3
 
0.7%
o 3
 
0.7%
Other values (4) 6
 
1.3%

toss_winner
Categorical

High correlation 

Distinct10
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
PBKS
13 
DC
8 
SRH
8 
RCB
7 
RR
7 
Other values (5)
31 

Length

Max length4
Median length3
Mean length2.7837838
Min length2

Characters and Unicode

Total characters206
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRCB
2nd rowRR
3rd rowCSK
4th rowDC
5th rowGT

Common Values

ValueCountFrequency (%)
PBKS 13
17.6%
DC 8
10.8%
SRH 8
10.8%
RCB 7
9.5%
RR 7
9.5%
GT 7
9.5%
MI 7
9.5%
CSK 6
8.1%
KKR 6
8.1%
LSG 5
 
6.8%

Length

2025-06-13T21:04:41.026647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:04:41.339099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
pbks 13
17.6%
dc 8
10.8%
srh 8
10.8%
rcb 7
9.5%
rr 7
9.5%
gt 7
9.5%
mi 7
9.5%
csk 6
8.1%
kkr 6
8.1%
lsg 5
 
6.8%

Most occurring characters

ValueCountFrequency (%)
R 35
17.0%
S 32
15.5%
K 31
15.0%
C 21
10.2%
B 20
9.7%
P 13
 
6.3%
G 12
 
5.8%
D 8
 
3.9%
H 8
 
3.9%
T 7
 
3.4%
Other values (3) 19
9.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 206
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 35
17.0%
S 32
15.5%
K 31
15.0%
C 21
10.2%
B 20
9.7%
P 13
 
6.3%
G 12
 
5.8%
D 8
 
3.9%
H 8
 
3.9%
T 7
 
3.4%
Other values (3) 19
9.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 206
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 35
17.0%
S 32
15.5%
K 31
15.0%
C 21
10.2%
B 20
9.7%
P 13
 
6.3%
G 12
 
5.8%
D 8
 
3.9%
H 8
 
3.9%
T 7
 
3.4%
Other values (3) 19
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 206
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 35
17.0%
S 32
15.5%
K 31
15.0%
C 21
10.2%
B 20
9.7%
P 13
 
6.3%
G 12
 
5.8%
D 8
 
3.9%
H 8
 
3.9%
T 7
 
3.4%
Other values (3) 19
9.2%

toss_decision
Categorical

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
Bowl
61 
Bat
13 

Length

Max length4
Median length4
Mean length3.8243243
Min length3

Characters and Unicode

Total characters283
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBowl
2nd rowBowl
3rd rowBowl
4th rowBowl
5th rowBowl

Common Values

ValueCountFrequency (%)
Bowl 61
82.4%
Bat 13
 
17.6%

Length

2025-06-13T21:04:41.979782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:04:42.241715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
bowl 61
82.4%
bat 13
 
17.6%

Most occurring characters

ValueCountFrequency (%)
B 74
26.1%
o 61
21.6%
w 61
21.6%
l 61
21.6%
a 13
 
4.6%
t 13
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 209
73.9%
Uppercase Letter 74
 
26.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 61
29.2%
w 61
29.2%
l 61
29.2%
a 13
 
6.2%
t 13
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
B 74
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 283
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 74
26.1%
o 61
21.6%
w 61
21.6%
l 61
21.6%
a 13
 
4.6%
t 13
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 283
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 74
26.1%
o 61
21.6%
w 61
21.6%
l 61
21.6%
a 13
 
4.6%
t 13
 
4.6%

first_ings_score
Real number (ℝ)

High correlation 

Distinct57
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.75342
Minimum95
Maximum286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-06-13T21:04:42.496278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile114.25
Q1166.75
median193
Q3212
95-th percentile239.75
Maximum286
Range191
Interquartile range (IQR)45.25

Descriptive statistics

Standard deviation37.231884
Coefficient of variation (CV)0.19621192
Kurtosis0.66838054
Mean189.75342
Median Absolute Deviation (MAD)23
Skewness-0.30641597
Sum14041.753
Variance1386.2132
MonotonicityNot monotonic
2025-06-13T21:04:42.808781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205 4
 
5.4%
180 3
 
4.1%
203 3
 
4.1%
190 3
 
4.1%
162 2
 
2.7%
163 2
 
2.7%
206 2
 
2.7%
217 2
 
2.7%
219 2
 
2.7%
196 2
 
2.7%
Other values (47) 49
66.2%
ValueCountFrequency (%)
95 1
1.4%
101 1
1.4%
103 1
1.4%
111 1
1.4%
116 1
1.4%
133 1
1.4%
143 1
1.4%
151 1
1.4%
152 1
1.4%
154 1
1.4%
ValueCountFrequency (%)
286 1
1.4%
278 1
1.4%
245 1
1.4%
243 1
1.4%
238 1
1.4%
236 1
1.4%
235 1
1.4%
231 1
1.4%
230 1
1.4%
228 1
1.4%

first_ings_wkts
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4520548
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-06-13T21:04:43.074403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median6
Q38
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0939646
Coefficient of variation (CV)0.32454228
Kurtosis-0.73172408
Mean6.4520548
Median Absolute Deviation (MAD)2
Skewness-0.082541906
Sum477.45205
Variance4.3846876
MonotonicityNot monotonic
2025-06-13T21:04:43.324371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 14
18.9%
6 13
17.6%
8 12
16.2%
9 8
10.8%
7 8
10.8%
10 6
8.1%
4 5
 
6.8%
3 5
 
6.8%
2 2
 
2.7%
6.452054795 1
 
1.4%
ValueCountFrequency (%)
2 2
 
2.7%
3 5
 
6.8%
4 5
 
6.8%
5 14
18.9%
6 13
17.6%
6.452054795 1
 
1.4%
7 8
10.8%
8 12
16.2%
9 8
10.8%
10 6
8.1%
ValueCountFrequency (%)
10 6
8.1%
9 8
10.8%
8 12
16.2%
7 8
10.8%
6.452054795 1
 
1.4%
6 13
17.6%
5 14
18.9%
4 5
 
6.8%
3 5
 
6.8%
2 2
 
2.7%

second_ings_score
Real number (ℝ)

High correlation 

Distinct52
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.01389
Minimum7
Maximum247
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-06-13T21:04:43.666792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile106.65
Q1158.25
median177
Q3200.5
95-th percentile230.7
Maximum247
Range240
Interquartile range (IQR)42.25

Descriptive statistics

Standard deviation38.269947
Coefficient of variation (CV)0.21992467
Kurtosis4.0340081
Mean174.01389
Median Absolute Deviation (MAD)20.5
Skewness-1.2847105
Sum12877.028
Variance1464.5889
MonotonicityNot monotonic
2025-06-13T21:04:44.195600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177 3
 
4.1%
159 3
 
4.1%
208 2
 
2.7%
161 2
 
2.7%
188 2
 
2.7%
205 2
 
2.7%
168 2
 
2.7%
174.0138889 2
 
2.7%
186 2
 
2.7%
147 2
 
2.7%
Other values (42) 52
70.3%
ValueCountFrequency (%)
7 1
1.4%
95 1
1.4%
98 1
1.4%
106 1
1.4%
107 1
1.4%
117 1
1.4%
120 1
1.4%
121 2
2.7%
146 2
2.7%
147 2
2.7%
ValueCountFrequency (%)
247 1
1.4%
242 1
1.4%
234 1
1.4%
232 1
1.4%
230 1
1.4%
212 1
1.4%
211 2
2.7%
209 2
2.7%
208 2
2.7%
207 1
1.4%

second_ings_wkts
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5138889
Minimum0
Maximum10
Zeros2
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-06-13T21:04:44.619703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.65
Q13
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8428861
Coefficient of variation (CV)0.5155864
Kurtosis-0.96715063
Mean5.5138889
Median Absolute Deviation (MAD)2
Skewness0.11377132
Sum408.02778
Variance8.0820015
MonotonicityNot monotonic
2025-06-13T21:04:44.869705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 11
14.9%
10 10
13.5%
2 9
12.2%
6 8
10.8%
4 8
10.8%
3 7
9.5%
9 6
8.1%
7 5
6.8%
8 4
 
5.4%
1 2
 
2.7%
Other values (2) 4
 
5.4%
ValueCountFrequency (%)
0 2
 
2.7%
1 2
 
2.7%
2 9
12.2%
3 7
9.5%
4 8
10.8%
5 11
14.9%
5.513888889 2
 
2.7%
6 8
10.8%
7 5
6.8%
8 4
 
5.4%
ValueCountFrequency (%)
10 10
13.5%
9 6
8.1%
8 4
 
5.4%
7 5
6.8%
6 8
10.8%
5.513888889 2
 
2.7%
5 11
14.9%
4 8
10.8%
3 7
9.5%
2 9
12.2%

match_result
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
completed
71 
tied
 
3

Length

Max length9
Median length9
Mean length8.7972973
Min length4

Characters and Unicode

Total characters651
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcompleted
2nd rowcompleted
3rd rowcompleted
4th rowcompleted
5th rowcompleted

Common Values

ValueCountFrequency (%)
completed 71
95.9%
tied 3
 
4.1%

Length

2025-06-13T21:04:45.135358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:04:45.354078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
completed 71
95.9%
tied 3
 
4.1%

Most occurring characters

ValueCountFrequency (%)
e 145
22.3%
t 74
11.4%
d 74
11.4%
c 71
10.9%
o 71
10.9%
m 71
10.9%
p 71
10.9%
l 71
10.9%
i 3
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 651
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 145
22.3%
t 74
11.4%
d 74
11.4%
c 71
10.9%
o 71
10.9%
m 71
10.9%
p 71
10.9%
l 71
10.9%
i 3
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 651
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 145
22.3%
t 74
11.4%
d 74
11.4%
c 71
10.9%
o 71
10.9%
m 71
10.9%
p 71
10.9%
l 71
10.9%
i 3
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 145
22.3%
t 74
11.4%
d 74
11.4%
c 71
10.9%
o 71
10.9%
m 71
10.9%
p 71
10.9%
l 71
10.9%
i 3
 
0.5%

match_winner
Categorical

Distinct10
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
PBKS
14 
RCB
10 
GT
9 
DC
8 
MI
8 
Other values (5)
25 

Length

Max length4
Median length3
Mean length2.7972973
Min length2

Characters and Unicode

Total characters207
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRCB
2nd rowSRH
3rd rowCSK
4th rowDC
5th rowPBKS

Common Values

ValueCountFrequency (%)
PBKS 14
18.9%
RCB 10
13.5%
GT 9
12.2%
DC 8
10.8%
MI 8
10.8%
SRH 6
8.1%
LSG 6
8.1%
KKR 5
 
6.8%
CSK 4
 
5.4%
RR 4
 
5.4%

Length

2025-06-13T21:04:45.635250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-13T21:04:45.947722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
pbks 14
18.9%
rcb 10
13.5%
gt 9
12.2%
dc 8
10.8%
mi 8
10.8%
srh 6
8.1%
lsg 6
8.1%
kkr 5
 
6.8%
csk 4
 
5.4%
rr 4
 
5.4%

Most occurring characters

ValueCountFrequency (%)
S 30
14.5%
R 29
14.0%
K 28
13.5%
B 24
11.6%
C 22
10.6%
G 15
7.2%
P 14
6.8%
T 9
 
4.3%
D 8
 
3.9%
M 8
 
3.9%
Other values (3) 20
9.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 207
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 30
14.5%
R 29
14.0%
K 28
13.5%
B 24
11.6%
C 22
10.6%
G 15
7.2%
P 14
6.8%
T 9
 
4.3%
D 8
 
3.9%
M 8
 
3.9%
Other values (3) 20
9.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 207
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 30
14.5%
R 29
14.0%
K 28
13.5%
B 24
11.6%
C 22
10.6%
G 15
7.2%
P 14
6.8%
T 9
 
4.3%
D 8
 
3.9%
M 8
 
3.9%
Other values (3) 20
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 30
14.5%
R 29
14.0%
K 28
13.5%
B 24
11.6%
C 22
10.6%
G 15
7.2%
P 14
6.8%
T 9
 
4.3%
D 8
 
3.9%
M 8
 
3.9%
Other values (3) 20
9.7%

wb_runs
Real number (ℝ)

Distinct28
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.181818
Minimum1
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-06-13T21:04:46.290066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.3
Q133.181818
median33.181818
Q333.181818
95-th percentile66.35
Maximum110
Range109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.19864
Coefficient of variation (CV)0.57858916
Kurtosis4.9056585
Mean33.181818
Median Absolute Deviation (MAD)0
Skewness1.573801
Sum2455.4545
Variance368.5878
MonotonicityNot monotonic
2025-06-13T21:04:46.572561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
33.18181818 41
55.4%
12 3
 
4.1%
11 2
 
2.7%
50 2
 
2.7%
6 2
 
2.7%
2 2
 
2.7%
100 1
 
1.4%
110 1
 
1.4%
83 1
 
1.4%
42 1
 
1.4%
Other values (18) 18
24.3%
ValueCountFrequency (%)
1 1
 
1.4%
2 2
2.7%
4 1
 
1.4%
6 2
2.7%
10 1
 
1.4%
11 2
2.7%
12 3
4.1%
14 1
 
1.4%
16 1
 
1.4%
18 1
 
1.4%
ValueCountFrequency (%)
110 1
1.4%
100 1
1.4%
83 1
1.4%
80 1
1.4%
59 1
1.4%
58 1
1.4%
54 1
1.4%
50 2
2.7%
44 1
1.4%
42 1
1.4%

wb_wickets
Real number (ℝ)

Distinct11
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3243243
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-06-13T21:04:46.806978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16.3243243
median6.3243243
Q36.8310811
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)0.50675676

Descriptive statistics

Standard deviation1.3853841
Coefficient of variation (CV)0.21905646
Kurtosis3.8812149
Mean6.3243243
Median Absolute Deviation (MAD)0.16216216
Skewness-0.98266488
Sum468
Variance1.9192892
MonotonicityNot monotonic
2025-06-13T21:04:47.025695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
6.324324324 37
50.0%
8 9
 
12.2%
7 7
 
9.5%
6 7
 
9.5%
5 5
 
6.8%
4 3
 
4.1%
9 2
 
2.7%
1 1
 
1.4%
3 1
 
1.4%
2 1
 
1.4%
ValueCountFrequency (%)
1 1
 
1.4%
2 1
 
1.4%
3 1
 
1.4%
4 3
 
4.1%
5 5
 
6.8%
6 7
 
9.5%
6.324324324 37
50.0%
7 7
 
9.5%
8 9
 
12.2%
9 2
 
2.7%
ValueCountFrequency (%)
10 1
 
1.4%
9 2
 
2.7%
8 9
 
12.2%
7 7
 
9.5%
6.324324324 37
50.0%
6 7
 
9.5%
5 5
 
6.8%
4 3
 
4.1%
3 1
 
1.4%
2 1
 
1.4%

balls_left
Real number (ℝ)

High correlation  Zeros 

Distinct27
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.819444
Minimum0
Maximum114
Zeros26
Zeros (%)35.1%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-06-13T21:04:47.260070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q313
95-th percentile33.9
Maximum114
Range114
Interquartile range (IQR)13

Descriptive statistics

Standard deviation17.431138
Coefficient of variation (CV)1.6110936
Kurtosis17.239859
Mean10.819444
Median Absolute Deviation (MAD)6
Skewness3.5903929
Sum800.63889
Variance303.84456
MonotonicityNot monotonic
2025-06-13T21:04:47.525527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 26
35.1%
9 4
 
5.4%
3 4
 
5.4%
6 3
 
4.1%
8 3
 
4.1%
13 3
 
4.1%
10 2
 
2.7%
10.81944444 2
 
2.7%
2 2
 
2.7%
26 2
 
2.7%
Other values (17) 23
31.1%
ValueCountFrequency (%)
0 26
35.1%
1 1
 
1.4%
2 2
 
2.7%
3 4
 
5.4%
4 2
 
2.7%
5 1
 
1.4%
6 3
 
4.1%
7 1
 
1.4%
8 3
 
4.1%
9 4
 
5.4%
ValueCountFrequency (%)
114 1
1.4%
60 1
1.4%
59 1
1.4%
43 1
1.4%
29 1
1.4%
26 2
2.7%
25 1
1.4%
24 1
1.4%
23 2
2.7%
22 2
2.7%

player_of_the_match
Text

Distinct53
Distinct (%)71.6%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2025-06-13T21:04:48.307310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length12.783784
Min length8

Characters and Unicode

Total characters946
Distinct characters45
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)50.0%

Sample

1st rowKrunal Pandya
2nd rowIshan Kishan
3rd rowNoor Ahmad
4th rowAshutosh Sharma
5th rowShreyas Iyer
ValueCountFrequency (%)
sharma 8
 
5.4%
krunal 6
 
4.0%
pandya 6
 
4.0%
iyer 3
 
2.0%
singh 3
 
2.0%
shreyas 3
 
2.0%
mitchell 3
 
2.0%
rahul 2
 
1.3%
vaibhav 2
 
1.3%
josh 2
 
1.3%
Other values (87) 111
74.5%
2025-06-13T21:04:49.293065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 128
 
13.5%
h 75
 
7.9%
75
 
7.9%
r 74
 
7.8%
i 58
 
6.1%
n 50
 
5.3%
s 43
 
4.5%
l 42
 
4.4%
e 40
 
4.2%
S 34
 
3.6%
Other values (35) 327
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 720
76.1%
Uppercase Letter 151
 
16.0%
Space Separator 75
 
7.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 128
17.8%
h 75
10.4%
r 74
10.3%
i 58
 
8.1%
n 50
 
6.9%
s 43
 
6.0%
l 42
 
5.8%
e 40
 
5.6%
u 29
 
4.0%
d 27
 
3.8%
Other values (14) 154
21.4%
Uppercase Letter
ValueCountFrequency (%)
S 34
22.5%
K 19
12.6%
P 16
10.6%
A 13
 
8.6%
R 12
 
7.9%
M 10
 
6.6%
J 7
 
4.6%
N 6
 
4.0%
I 6
 
4.0%
H 4
 
2.6%
Other values (10) 24
15.9%
Space Separator
ValueCountFrequency (%)
75
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 871
92.1%
Common 75
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 128
14.7%
h 75
 
8.6%
r 74
 
8.5%
i 58
 
6.7%
n 50
 
5.7%
s 43
 
4.9%
l 42
 
4.8%
e 40
 
4.6%
S 34
 
3.9%
u 29
 
3.3%
Other values (34) 298
34.2%
Common
ValueCountFrequency (%)
75
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 946
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 128
 
13.5%
h 75
 
7.9%
75
 
7.9%
r 74
 
7.8%
i 58
 
6.1%
n 50
 
5.3%
s 43
 
4.5%
l 42
 
4.4%
e 40
 
4.2%
S 34
 
3.6%
Other values (35) 327
34.6%

top_scorer
Text

Distinct41
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2025-06-13T21:04:49.993947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length16.5
Mean length13.135135
Min length8

Characters and Unicode

Total characters972
Distinct characters45
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)28.4%

Sample

1st rowVirat Kohli
2nd rowIshan Kishan
3rd rowRachin Ravindra
4th rowNicholas Pooran
5th rowShreyas Iyer
ValueCountFrequency (%)
nicholas 7
 
4.7%
pooran 7
 
4.7%
virat 4
 
2.7%
kohli 4
 
2.7%
sharma 4
 
2.7%
kishan 3
 
2.0%
singh 3
 
2.0%
iyer 3
 
2.0%
jaiswal 3
 
2.0%
yashasvi 3
 
2.0%
Other values (71) 109
72.7%
2025-06-13T21:04:51.025192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 133
13.7%
i 80
 
8.2%
h 76
 
7.8%
76
 
7.8%
s 59
 
6.1%
n 58
 
6.0%
r 57
 
5.9%
l 45
 
4.6%
o 35
 
3.6%
e 34
 
3.5%
Other values (35) 319
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 745
76.6%
Uppercase Letter 151
 
15.5%
Space Separator 76
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 133
17.9%
i 80
10.7%
h 76
10.2%
s 59
7.9%
n 58
7.8%
r 57
7.7%
l 45
 
6.0%
o 35
 
4.7%
e 34
 
4.6%
t 26
 
3.5%
Other values (13) 142
19.1%
Uppercase Letter
ValueCountFrequency (%)
S 27
17.9%
R 21
13.9%
P 17
11.3%
K 15
9.9%
N 12
7.9%
V 8
 
5.3%
I 7
 
4.6%
J 7
 
4.6%
A 6
 
4.0%
Y 5
 
3.3%
Other values (11) 26
17.2%
Space Separator
ValueCountFrequency (%)
76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 896
92.2%
Common 76
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 133
14.8%
i 80
 
8.9%
h 76
 
8.5%
s 59
 
6.6%
n 58
 
6.5%
r 57
 
6.4%
l 45
 
5.0%
o 35
 
3.9%
e 34
 
3.8%
S 27
 
3.0%
Other values (34) 292
32.6%
Common
ValueCountFrequency (%)
76
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 133
13.7%
i 80
 
8.2%
h 76
 
7.8%
76
 
7.8%
s 59
 
6.1%
n 58
 
6.0%
r 57
 
5.9%
l 45
 
4.6%
o 35
 
3.6%
e 34
 
3.5%
Other values (35) 319
32.8%

highscore
Real number (ℝ)

High correlation 

Distinct45
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.549296
Minimum37
Maximum141
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-06-13T21:04:51.322069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile47.9
Q161
median73
Q387.75
95-th percentile106.7
Maximum141
Range104
Interquartile range (IQR)26.75

Descriptive statistics

Standard deviation19.671124
Coefficient of variation (CV)0.26386733
Kurtosis0.81170109
Mean74.549296
Median Absolute Deviation (MAD)13.5
Skewness0.73905679
Sum5516.6479
Variance386.95312
MonotonicityNot monotonic
2025-06-13T21:04:51.618809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
73 5
 
6.8%
61 4
 
5.4%
57 3
 
4.1%
97 3
 
4.1%
70 3
 
4.1%
74.54929577 3
 
4.1%
67 3
 
4.1%
94 2
 
2.7%
87 2
 
2.7%
76 2
 
2.7%
Other values (35) 44
59.5%
ValueCountFrequency (%)
37 1
 
1.4%
40 1
 
1.4%
44 2
2.7%
50 1
 
1.4%
51 2
2.7%
52 1
 
1.4%
53 1
 
1.4%
56 1
 
1.4%
57 3
4.1%
58 2
2.7%
ValueCountFrequency (%)
141 1
 
1.4%
118 1
 
1.4%
117 1
 
1.4%
108 1
 
1.4%
106 1
 
1.4%
105 1
 
1.4%
103 1
 
1.4%
101 1
 
1.4%
97 3
4.1%
95 1
 
1.4%

best_bowling
Text

Distinct42
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2025-06-13T21:04:52.355687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length13.621622
Min length10

Characters and Unicode

Total characters1008
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)32.4%

Sample

1st rowKrunal Pandya
2nd rowTushar Deshpande
3rd rowNoor Ahmad
4th rowMitchell Starc
5th rowSai Kishore
ValueCountFrequency (%)
prasidh 8
 
5.4%
krishna 8
 
5.4%
pandya 5
 
3.4%
krunal 4
 
2.7%
arshdeep 4
 
2.7%
singh 4
 
2.7%
bumrah 3
 
2.0%
mitchell 3
 
2.0%
sharma 3
 
2.0%
hazlewood 3
 
2.0%
Other values (68) 103
69.6%
2025-06-13T21:04:53.419148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 138
13.7%
r 87
 
8.6%
h 83
 
8.2%
74
 
7.3%
n 55
 
5.5%
i 54
 
5.4%
e 50
 
5.0%
s 49
 
4.9%
d 41
 
4.1%
u 35
 
3.5%
Other values (36) 342
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 782
77.6%
Uppercase Letter 150
 
14.9%
Space Separator 74
 
7.3%
Other Punctuation 2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 138
17.6%
r 87
11.1%
h 83
10.6%
n 55
 
7.0%
i 54
 
6.9%
e 50
 
6.4%
s 49
 
6.3%
d 41
 
5.2%
u 35
 
4.5%
l 34
 
4.3%
Other values (13) 156
19.9%
Uppercase Letter
ValueCountFrequency (%)
K 23
15.3%
S 18
12.0%
P 17
11.3%
A 15
10.0%
M 10
 
6.7%
J 10
 
6.7%
H 9
 
6.0%
C 7
 
4.7%
B 7
 
4.7%
N 6
 
4.0%
Other values (11) 28
18.7%
Space Separator
ValueCountFrequency (%)
74
100.0%
Other Punctuation
ValueCountFrequency (%)
' 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 932
92.5%
Common 76
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 138
14.8%
r 87
 
9.3%
h 83
 
8.9%
n 55
 
5.9%
i 54
 
5.8%
e 50
 
5.4%
s 49
 
5.3%
d 41
 
4.4%
u 35
 
3.8%
l 34
 
3.6%
Other values (34) 306
32.8%
Common
ValueCountFrequency (%)
74
97.4%
' 2
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 138
13.7%
r 87
 
8.6%
h 83
 
8.2%
74
 
7.3%
n 55
 
5.5%
i 54
 
5.4%
e 50
 
5.0%
s 49
 
4.9%
d 41
 
4.1%
u 35
 
3.5%
Other values (36) 342
33.9%

best_bowling_figure
Text

Distinct54
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2025-06-13T21:04:53.977623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9864865
Min length4

Characters and Unicode

Total characters369
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)54.1%

Sample

1st row3--29
2nd row3--44
3rd row4--18
4th row3--42
5th row3--30
ValueCountFrequency (%)
2--25 7
 
9.5%
3--29 3
 
4.1%
3--24 2
 
2.7%
3--30 2
 
2.7%
2--17 2
 
2.7%
3--21 2
 
2.7%
3--36 2
 
2.7%
2--18 2
 
2.7%
4--28 2
 
2.7%
2--32 2
 
2.7%
Other values (44) 48
64.9%
2025-06-13T21:04:54.930589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 148
40.1%
2 61
16.5%
3 58
 
15.7%
4 33
 
8.9%
1 21
 
5.7%
5 16
 
4.3%
8 8
 
2.2%
7 8
 
2.2%
9 6
 
1.6%
6 6
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 221
59.9%
Dash Punctuation 148
40.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 61
27.6%
3 58
26.2%
4 33
14.9%
1 21
 
9.5%
5 16
 
7.2%
8 8
 
3.6%
7 8
 
3.6%
9 6
 
2.7%
6 6
 
2.7%
0 4
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 148
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 369
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 148
40.1%
2 61
16.5%
3 58
 
15.7%
4 33
 
8.9%
1 21
 
5.7%
5 16
 
4.3%
8 8
 
2.2%
7 8
 
2.2%
9 6
 
1.6%
6 6
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 369
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 148
40.1%
2 61
16.5%
3 58
 
15.7%
4 33
 
8.9%
1 21
 
5.7%
5 16
 
4.3%
8 8
 
2.2%
7 8
 
2.2%
9 6
 
1.6%
6 6
 
1.6%

Interactions

2025-06-13T21:04:33.121670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:18.327920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:20.297512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-06-13T21:04:25.060451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:26.982010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:28.699372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:30.378101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:32.393593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-06-13T21:04:23.482508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:25.263557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:27.169495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:28.871257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:30.558445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:32.574833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:35.135930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:19.733184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:21.857868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:23.669970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:25.466523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:27.356996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:29.043133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:30.792804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:32.762292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:35.307797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:19.953307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:22.048193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:23.857469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:25.685279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:27.544377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:29.230628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:31.120929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-13T21:04:32.934205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-06-13T21:04:55.274382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
balls_leftfirst_ings_scorefirst_ings_wktshighscorematch_idmatch_resultmatch_winnersecond_ings_scoresecond_ings_wktsstageteam1team2toss_decisiontoss_winnervenuewb_runswb_wickets
balls_left1.000-0.5390.271-0.223-0.0520.5160.000-0.453-0.5070.1160.0370.0000.2040.0000.0000.2630.322
first_ings_score-0.5391.000-0.5950.5290.2010.2520.1890.6520.4440.0000.1810.0760.0000.0000.1760.040-0.098
first_ings_wkts0.271-0.5951.000-0.436-0.2720.5470.139-0.317-0.2150.0000.1690.1140.2030.1050.187-0.052-0.090
highscore-0.2230.529-0.4361.0000.0470.0680.0360.552-0.0590.1030.0000.0000.2260.0000.000-0.1210.196
match_id-0.0520.201-0.2720.0471.0000.2890.0000.1420.1340.3400.0000.0000.1840.0000.0000.029-0.137
match_result0.5160.2520.5470.0680.2891.0000.2420.5260.2470.0000.0000.0000.0000.0000.0000.0000.000
match_winner0.0000.1890.1390.0360.0000.2421.0000.0000.0000.0000.2630.4670.1360.4980.1960.1150.000
second_ings_score-0.4530.652-0.3170.5520.1420.5260.0001.000-0.0080.0000.0000.0000.1920.0000.000-0.421-0.106
second_ings_wkts-0.5070.444-0.215-0.0590.1340.2470.000-0.0081.0000.0460.0930.1260.0000.1560.1040.139-0.475
stage0.1160.0000.0000.1030.3400.0000.0000.0000.0461.0000.1040.1040.0000.0000.0130.0000.000
team10.0370.1810.1690.0000.0000.0000.2630.0000.0930.1041.0000.0000.1850.2290.8640.0640.047
team20.0000.0760.1140.0000.0000.0000.4670.0000.1260.1040.0001.0000.0340.5620.0000.1080.003
toss_decision0.2040.0000.2030.2260.1840.0000.1360.1920.0000.0000.1850.0341.0000.1060.1940.2290.000
toss_winner0.0000.0000.1050.0000.0000.0000.4980.0000.1560.0000.2290.5620.1061.0000.2280.0490.000
venue0.0000.1760.1870.0000.0000.0000.1960.0000.1040.0130.8640.0000.1940.2281.0000.0280.130
wb_runs0.2630.040-0.052-0.1210.0290.0000.115-0.4210.1390.0000.0640.1080.2290.0490.0281.0000.002
wb_wickets0.322-0.098-0.0900.196-0.1370.0000.000-0.106-0.4750.0000.0470.0030.0000.0000.1300.0021.000

Missing values

2025-06-13T21:04:35.652443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-13T21:04:36.365607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

match_iddatevenueteam1team2stagetoss_winnertoss_decisionfirst_ings_scorefirst_ings_wktssecond_ings_scoresecond_ings_wktsmatch_resultmatch_winnerwb_runswb_wicketsballs_leftplayer_of_the_matchtop_scorerhighscorebest_bowlingbest_bowling_figure
01March 22,2025Eden Gardens, KolkataKKRRCBLeagueRCBBowl174.08.0177.03.0completedRCB33.1818187.00000022.0Krunal PandyaVirat Kohli59.0Krunal Pandya3--29
12March 23,2025Rajiv Gandhi International Stadium, HyderabadSRHRRLeagueRRBowl286.06.0242.06.0completedSRH44.0000006.3243240.0Ishan KishanIshan Kishan106.0Tushar Deshpande3--44
23March 23,2025MA Chidambaram Stadium, ChennaiCSKMILeagueCSKBowl155.09.0158.06.0completedCSK33.1818184.0000005.0Noor AhmadRachin Ravindra65.0Noor Ahmad4--18
34March 24,2025ACA-VDCA Cricket Stadium, VishakhapatnamDCLSGLeagueDCBowl209.08.0211.09.0completedDC33.1818181.0000003.0Ashutosh SharmaNicholas Pooran75.0Mitchell Starc3--42
45March 25,2025Narendra Modi Stadium, AhmedabadGTPBKSLeagueGTBowl243.05.0232.05.0completedPBKS11.0000006.3243240.0Shreyas IyerShreyas Iyer97.0Sai Kishore3--30
56March 26,2025Barsapara Stadium, GuwahatiRRKKRLeagueKKRBowl151.09.0153.02.0completedKKR33.1818188.00000015.0Quinton de KockQuinton de Kock97.0Varun Chakravarthy2--17
67March 27,2025Rajiv Gandhi International Stadium, HyderabadSRHLSGLeagueLSGBowl190.09.0193.05.0completedLSG33.1818185.00000023.0Shardul ThakurNicholas Pooran70.0Shardul Thakur4--34
78March 28,2025MA Chidambaram Stadium, ChennaiCSKRCBLeagueCSKBowl196.07.0146.08.0completedRCB50.0000006.3243240.0Rajat PatidarRajat Patidar51.0Josh Hazlewood3--21
89March 29,2025Narendra Modi Stadium, AhmedabadGTMILeagueMIBowl196.08.0160.06.0completedGT36.0000006.3243240.0Prasidh KrishnaSai Sudarshan63.0Prasidh Krishna2--18
910March 30,2025ACA-VDCA Cricket Stadium, VishakhapatnamDCSRHLeagueSRHBat163.010.0166.03.0completedDC33.1818187.00000024.0Mitchell StarcAniket Verma74.0Mitchell Starc5--35
match_iddatevenueteam1team2stagetoss_winnertoss_decisionfirst_ings_scorefirst_ings_wktssecond_ings_scoresecond_ings_wktsmatch_resultmatch_winnerwb_runswb_wicketsballs_leftplayer_of_the_matchtop_scorerhighscorebest_bowlingbest_bowling_figure
6465May 23,2025Ekana Cricket Stadium, LucknowRCBSRHLeagueRCBBowl231.06.0189.010.0completedSRH42.0000006.3243241.0Ishan KishanIshan Kishan94.0Pat Cummins3--28
6566May 24,2025Sawai Mansingh Stadium, JaipurPBKSDCLeagueDCBowl206.08.0208.04.0completedDC33.1818186.0000003.0Sameer RizviSameer Rizvi58.0Mustafizur Rahman3--33
6667May 25,2025Narendra Modi Stadium, AhmedabadGTCSKLeagueCSKBat230.05.0147.010.0completedCSK83.0000006.3243249.0Dewald BrevisDewald Brevis57.0Noor Ahmad3--21
6768May 25,2025Arun Jaitley Stadium, DelhiKKRSRHLeagueSRHBat278.03.0168.010.0completedSRH110.0000006.3243248.0Heinrich KlassenHeinrich Klassen105.0Jaydev Unadkat3--24
6869May 26,2025Sawai Mansingh Stadium, JaipurPBKSMILeaguePBKSBowl184.07.0187.03.0completedPBKS33.1818187.0000009.0Josh InglisJosh Inglis73.0Arshdeep Singh2--28
6970May 27,2025Ekana Cricket Stadium, LucknowLSGRCBLeagueRCBBowl227.03.0230.04.0completedRCB33.1818186.0000008.0Jitesh SharmaRishabh Pant118.0Will O'Rourke2--74
7071May 29,2025New PCA Cricket Stadium, MullanpurPBKSRCBPlayoffsRCBBowl101.010.0106.02.0completedRCB33.1818188.00000060.0Suyash SharmaPhil Salt56.0Suyash Sharma3--17
7172May 30,2025New PCA Cricket Stadium, MullanpurGTMIPlayoffsMIBat228.05.0208.06.0completedMI20.0000006.3243240.0Rohit SharmaRohit Sharma81.0Sai Kishore2--42
7273June 1,2025Narendra Modi Stadium, AhmedabadPBKSMIPlayoffsPBKSBowl203.06.0207.05.0completedPBKS33.1818185.0000006.0Shreyas IyerShreyas Iyer87.0Azmatullah Omarzai2--43
7374June 3,2025Narendra Modi Stadium, AhmedabadRCBPBKSFinalPBKSBowl190.09.0184.07.0completedRCB6.0000006.3243240.0Krunal PandyaShashank Singh61.0Arshdeep Singh3--40